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Author*The author of this computation has been verified*
R Software Modulerwasp_pairs.wasp
Title produced by softwareKendall tau Correlation Matrix
Date of computationSat, 11 Dec 2010 11:29:07 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/11/t1292066835g07nntw8v40verd.htm/, Retrieved Mon, 06 May 2024 21:51:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108069, Retrieved Mon, 06 May 2024 21:51:16 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact184
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Kendall tau Correlation Matrix] [WS10 - Pearson Co...] [2010-12-11 11:29:07] [934c3727858e074bf543f25f5906ed72] [Current]
-    D    [Kendall tau Correlation Matrix] [cha] [2010-12-14 19:49:28] [74be16979710d4c4e7c6647856088456]
-    D    [Kendall tau Correlation Matrix] [Pearson Correlatie] [2010-12-18 17:49:30] [8ef49741e164ec6343c90c7935194465]
-   PD    [Kendall tau Correlation Matrix] [Kendall’s tau C...] [2010-12-18 17:50:49] [8ef49741e164ec6343c90c7935194465]
-           [Kendall tau Correlation Matrix] [Kendall's tau Cor...] [2010-12-18 18:02:04] [8ef49741e164ec6343c90c7935194465]
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Dataseries X:
104.37	1	167.16	101.56	100.93
104.89	2	179.84	102.13	101.18
105.15	3	174.44	102.39	101.11
105.72	4	180.35	102.42	102.42
106.38	5	193.17	103.87	102.37
106.40	6	195.16	104.44	101.95
106.47	7	202.43	104.97	102.20
106.59	8	189.91	105.17	103.35
106.76	9	195.98	105.35	103.65
107.35	10	212.09	104.65	102.06
107.81	11	205.81	106.62	102.66
108.03	12	204.31	107.05	102.32
109.08	1	196.07	112.30	102.21
109.86	2	199.98	114.70	102.33
110.29	3	199.1	115.40	104.41
110.34	4	198.31	115.64	104.33
110.59	5	195.72	115.66	105.27
110.64	6	223.04	114.50	105.34
110.83	7	238.41	115.14	104.88
111.51	8	259.73	115.41	105.49
113.32	9	326.54	119.32	105.90
115.89	10	335.15	124.77	105.39
116.51	11	321.81	130.96	104.40
117.44	12	368.62	141.02	106.19
118.25	1	369.59	150.60	106.54
118.65	2	425	151.10	108.26
118.52	3	439.72	157.19	106.95
119.07	4	362.23	157.28	108.32
119.12	5	328.76	156.54	108.35
119.28	6	348.55	159.62	109.29
119.30	7	328.18	163.77	109.46
119.44	8	329.34	165.08	109.50
119.57	9	295.55	164.75	109.84
119.93	10	237.38	163.93	108.73
120.03	11	226.85	157.51	109.38
119.66	12	220.14	153.36	109.97
119.46	1	239.36	156.83	111.10
119.48	2	224.69	154.98	110.53
119.56	3	230.98	155.02	110.23
119.43	4	233.47	153.34	109.41
119.57	5	256.7	153.19	108.94
119.59	6	253.41	152.80	109.81
119.50	7	224.95	152.97	109.20
119.54	8	210.37	152.96	109.45
119.56	9	191.09	152.35	110.61
119.61	10	198.85	151.88	109.44
119.64	11	211.04	150.27	109.77
119.60	12	206.25	148.80	108.04
119.71	1	201.19	149.28	109.65
119.72	2	194.37	148.64	111.69
119.66	3	191.08	150.36	111.65
119.76	4	192.87	149.69	112.04
119.80	5	181.61	152.94	111.42
119.88	6	157.67	155.18	112.25
119.78	7	196.14	156.32	111.46
120.08	8	246.35	156.25	111.62
120.22	9	271.9 	155.52	111.77




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time20 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 20 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108069&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]20 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108069&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108069&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time20 seconds
R Server'George Udny Yule' @ 72.249.76.132







Correlations for all pairs of data series (method=pearson)
BroodMaandTarweMeelWater
Brood10.1040.3830.9740.937
Maand0.10410.0530.050.039
Tarwe0.3830.05310.4030.162
Meel0.9740.050.40310.917
Water0.9370.0390.1620.9171

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series (method=pearson) \tabularnewline
  & Brood & Maand & Tarwe & Meel & Water \tabularnewline
Brood & 1 & 0.104 & 0.383 & 0.974 & 0.937 \tabularnewline
Maand & 0.104 & 1 & 0.053 & 0.05 & 0.039 \tabularnewline
Tarwe & 0.383 & 0.053 & 1 & 0.403 & 0.162 \tabularnewline
Meel & 0.974 & 0.05 & 0.403 & 1 & 0.917 \tabularnewline
Water & 0.937 & 0.039 & 0.162 & 0.917 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108069&T=1

[TABLE]
[ROW][C]Correlations for all pairs of data series (method=pearson)[/C][/ROW]
[ROW][C] [/C][C]Brood[/C][C]Maand[/C][C]Tarwe[/C][C]Meel[/C][C]Water[/C][/ROW]
[ROW][C]Brood[/C][C]1[/C][C]0.104[/C][C]0.383[/C][C]0.974[/C][C]0.937[/C][/ROW]
[ROW][C]Maand[/C][C]0.104[/C][C]1[/C][C]0.053[/C][C]0.05[/C][C]0.039[/C][/ROW]
[ROW][C]Tarwe[/C][C]0.383[/C][C]0.053[/C][C]1[/C][C]0.403[/C][C]0.162[/C][/ROW]
[ROW][C]Meel[/C][C]0.974[/C][C]0.05[/C][C]0.403[/C][C]1[/C][C]0.917[/C][/ROW]
[ROW][C]Water[/C][C]0.937[/C][C]0.039[/C][C]0.162[/C][C]0.917[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108069&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108069&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Correlations for all pairs of data series (method=pearson)
BroodMaandTarweMeelWater
Brood10.1040.3830.9740.937
Maand0.10410.0530.050.039
Tarwe0.3830.05310.4030.162
Meel0.9740.050.40310.917
Water0.9370.0390.1620.9171







Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Brood;Maand0.10390.17660.1617
p-value(0.4418)(0.1889)(0.0852)
Brood;Tarwe0.38310.19710.1286
p-value(0.0033)(0.1417)(0.1582)
Brood;Meel0.97360.77390.614
p-value(0)(0)(0)
Brood;Water0.93690.91890.7796
p-value(0)(0)(0)
Maand;Tarwe0.05260.20480.1402
p-value(0.6976)(0.1264)(0.1354)
Maand;Meel0.04950.09680.0831
p-value(0.7144)(0.4737)(0.3762)
Maand;Water0.03910.02750.0286
p-value(0.7728)(0.8392)(0.761)
Tarwe;Meel0.40340.55450.4048
p-value(0.0019)(0)(0)
Tarwe;Water0.16170.16460.1115
p-value(0.2295)(0.2203)(0.2205)
Meel;Water0.91740.76960.589
p-value(0)(0)(0)

\begin{tabular}{lllllllll}
\hline
Correlations for all pairs of data series with p-values \tabularnewline
pair & Pearson r & Spearman rho & Kendall tau \tabularnewline
Brood;Maand & 0.1039 & 0.1766 & 0.1617 \tabularnewline
p-value & (0.4418) & (0.1889) & (0.0852) \tabularnewline
Brood;Tarwe & 0.3831 & 0.1971 & 0.1286 \tabularnewline
p-value & (0.0033) & (0.1417) & (0.1582) \tabularnewline
Brood;Meel & 0.9736 & 0.7739 & 0.614 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Brood;Water & 0.9369 & 0.9189 & 0.7796 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
Maand;Tarwe & 0.0526 & 0.2048 & 0.1402 \tabularnewline
p-value & (0.6976) & (0.1264) & (0.1354) \tabularnewline
Maand;Meel & 0.0495 & 0.0968 & 0.0831 \tabularnewline
p-value & (0.7144) & (0.4737) & (0.3762) \tabularnewline
Maand;Water & 0.0391 & 0.0275 & 0.0286 \tabularnewline
p-value & (0.7728) & (0.8392) & (0.761) \tabularnewline
Tarwe;Meel & 0.4034 & 0.5545 & 0.4048 \tabularnewline
p-value & (0.0019) & (0) & (0) \tabularnewline
Tarwe;Water & 0.1617 & 0.1646 & 0.1115 \tabularnewline
p-value & (0.2295) & (0.2203) & (0.2205) \tabularnewline
Meel;Water & 0.9174 & 0.7696 & 0.589 \tabularnewline
p-value & (0) & (0) & (0) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108069&T=2

[TABLE]
[ROW][C]Correlations for all pairs of data series with p-values[/C][/ROW]
[ROW][C]pair[/C][C]Pearson r[/C][C]Spearman rho[/C][C]Kendall tau[/C][/ROW]
[ROW][C]Brood;Maand[/C][C]0.1039[/C][C]0.1766[/C][C]0.1617[/C][/ROW]
[ROW][C]p-value[/C][C](0.4418)[/C][C](0.1889)[/C][C](0.0852)[/C][/ROW]
[ROW][C]Brood;Tarwe[/C][C]0.3831[/C][C]0.1971[/C][C]0.1286[/C][/ROW]
[ROW][C]p-value[/C][C](0.0033)[/C][C](0.1417)[/C][C](0.1582)[/C][/ROW]
[ROW][C]Brood;Meel[/C][C]0.9736[/C][C]0.7739[/C][C]0.614[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Brood;Water[/C][C]0.9369[/C][C]0.9189[/C][C]0.7796[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Maand;Tarwe[/C][C]0.0526[/C][C]0.2048[/C][C]0.1402[/C][/ROW]
[ROW][C]p-value[/C][C](0.6976)[/C][C](0.1264)[/C][C](0.1354)[/C][/ROW]
[ROW][C]Maand;Meel[/C][C]0.0495[/C][C]0.0968[/C][C]0.0831[/C][/ROW]
[ROW][C]p-value[/C][C](0.7144)[/C][C](0.4737)[/C][C](0.3762)[/C][/ROW]
[ROW][C]Maand;Water[/C][C]0.0391[/C][C]0.0275[/C][C]0.0286[/C][/ROW]
[ROW][C]p-value[/C][C](0.7728)[/C][C](0.8392)[/C][C](0.761)[/C][/ROW]
[ROW][C]Tarwe;Meel[/C][C]0.4034[/C][C]0.5545[/C][C]0.4048[/C][/ROW]
[ROW][C]p-value[/C][C](0.0019)[/C][C](0)[/C][C](0)[/C][/ROW]
[ROW][C]Tarwe;Water[/C][C]0.1617[/C][C]0.1646[/C][C]0.1115[/C][/ROW]
[ROW][C]p-value[/C][C](0.2295)[/C][C](0.2203)[/C][C](0.2205)[/C][/ROW]
[ROW][C]Meel;Water[/C][C]0.9174[/C][C]0.7696[/C][C]0.589[/C][/ROW]
[ROW][C]p-value[/C][C](0)[/C][C](0)[/C][C](0)[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108069&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108069&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Correlations for all pairs of data series with p-values
pairPearson rSpearman rhoKendall tau
Brood;Maand0.10390.17660.1617
p-value(0.4418)(0.1889)(0.0852)
Brood;Tarwe0.38310.19710.1286
p-value(0.0033)(0.1417)(0.1582)
Brood;Meel0.97360.77390.614
p-value(0)(0)(0)
Brood;Water0.93690.91890.7796
p-value(0)(0)(0)
Maand;Tarwe0.05260.20480.1402
p-value(0.6976)(0.1264)(0.1354)
Maand;Meel0.04950.09680.0831
p-value(0.7144)(0.4737)(0.3762)
Maand;Water0.03910.02750.0286
p-value(0.7728)(0.8392)(0.761)
Tarwe;Meel0.40340.55450.4048
p-value(0.0019)(0)(0)
Tarwe;Water0.16170.16460.1115
p-value(0.2295)(0.2203)(0.2205)
Meel;Water0.91740.76960.589
p-value(0)(0)(0)



Parameters (Session):
par1 = pearson ;
Parameters (R input):
par1 = pearson ;
R code (references can be found in the software module):
panel.tau <- function(x, y, digits=2, prefix='', cex.cor)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
rr <- cor.test(x, y, method=par1)
r <- round(rr$p.value,2)
txt <- format(c(r, 0.123456789), digits=digits)[1]
txt <- paste(prefix, txt, sep='')
if(missing(cex.cor)) cex <- 0.5/strwidth(txt)
text(0.5, 0.5, txt, cex = cex)
}
panel.hist <- function(x, ...)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
}
bitmap(file='test1.png')
pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
dev.off()
load(file='createtable')
n <- length(y[,1])
n
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ',header=TRUE)
for (i in 1:n) {
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
for (j in 1:n) {
r <- cor.test(y[i,],y[j,],method=par1)
a<-table.element(a,round(r$estimate,3))
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'pair',1,TRUE)
a<-table.element(a,'Pearson r',1,TRUE)
a<-table.element(a,'Spearman rho',1,TRUE)
a<-table.element(a,'Kendall tau',1,TRUE)
a<-table.row.end(a)
cor.test(y[1,],y[2,],method=par1)
for (i in 1:(n-1))
{
for (j in (i+1):n)
{
a<-table.row.start(a)
dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='')
a<-table.element(a,dum,header=TRUE)
rp <- cor.test(y[i,],y[j,],method='pearson')
a<-table.element(a,round(rp$estimate,4))
rs <- cor.test(y[i,],y[j,],method='spearman')
a<-table.element(a,round(rs$estimate,4))
rk <- cor.test(y[i,],y[j,],method='kendall')
a<-table.element(a,round(rk$estimate,4))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=T)
a<-table.element(a,paste('(',round(rp$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rs$p.value,4),')',sep=''))
a<-table.element(a,paste('(',round(rk$p.value,4),')',sep=''))
a<-table.row.end(a)
}
}
a<-table.end(a)
table.save(a,file='mytable1.tab')